A Model Registry is a centralized system that manages and stores machine learning models throughout their lifecycle. It serves as a repository where models can be registered, versioned, and tracked, ensuring that data scientists and engineers can easily access and utilize them for inference or further development. By maintaining metadata about each model, such as training data and performance metrics, a Model Registry facilitates collaboration and reproducibility in machine learning projects. This tool is essential for effective MLOps, enabling teams to streamline model deployment and management processes while ensuring compliance and governance standards are met.